Conventionally the use of clustering for unsupervised learning of features in time series focuses on grouping data points into a fixed number of clusters. In the field of prognostics and health management it is generally assumed that grouping corresponds to a degree of component wear/degradation with respect to a particular type of fault mode.
Conventionally, clustering time series data for modeling and prediction with respect to prognostics, diagnostics or remaining useful life prediction is performed several different ways. For example, subtractive-maximum entropy fuzzy clustering may be used as a form of unsupervised feature learning. The subtractive-maximum entropy fuzzy clustering operates on multidimensional time series data and associated each cluster with a component condition. With subtractive-maximum entropy fuzzy clustering, the number of clusters is predetermined using subtractive clustering and, therefore, any available information from cluster stability is lost.
As another example, competitive model based clustering utilizes hidden Markov models to represent time series with different characteristics. As a condition of a component degrades the time series of measurements generated by the component as different features. The hidden Markov models compete to represent segments of the time series and, ultimately, partition the time series into groups of segments generated by different operating conditions. Supervised learning is then used to map each set of segments to a degree of component degradation. A disadvantage of competitive model based clustering is that it focuses on one dimensional time series data due to the challenge of modeling multi-dimensional time series data accurately with hidden Markov models.
A further example of clustering time series focuses on clustering different segments of one dimensional time series such as with dynamic time warping as the metric between time series segments. A standard clustering algorithm is then applied to the set of segments. A unique predictive model is then learned for each cluster of data.